Machine learning method in the detection of adverse drug reaction signals of brigatinib based on FAERS database

doi:10.19803/j.1672-8629.20220645

Abstract

Abstract: Objective To evaluate the performance of machine learning algorithms in detecting signals of adverse drug reactions (ADR) of brigatinib. Methods Data on signals of adverse drug reaction of brigatinib retrieved from the FDA FAERS from April 1, 2017 to Match 31, 2022 was used. An input dataset was constructed for the drug to be studied, including known ADR listed in drug labels and unknown ADR. For the known ADR, four machine learning algorithms (Random Forest, XGBoost, Logistic Regression, and kNN) were trained and evaluated by the area under the curve (AUC) to compare the machine learning algorithms with traditional disproportionality analysis method involving the reporting odds ratio (ROR) and information component (IC) . Results Among these methods, the kNN algorithm had the largest AUC with an average value of 0.875, followed by Logistic regression (0.852), XGBoost (0.722), random forest (0.662) and DPA (0.548). In the unknown ADR datasets, the machine learning model established by the kNN algorithm detected 6 additional signals (15.8%), compared with 4 additional signals (10.5%) by the DPA method. Conclusion The machine learning model established by the kNN algorithm has better performance than the traditional DPA method in detecting ADR signals.

Key words: disproportionality analysis, brigatinib, signal detection, adverse drug reaction, machine learning algorithm, FAERS

CLC Number:

R979.1
R994

CHEN Xiao, GUO Xiaojing, XU Jinfang, WEI Lianhui, CHEN Chenxin, LIANG Jizhou, ZHENG Yi, YE Xiaofei. Machine learning method in the detection of adverse drug reaction signals of brigatinib based on FAERS database[J]. Chinese Journal of Pharmacovigilance, 2023, 20(6): 639-645.

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